Increased Pain Variability in Patients With Chronic Pain: A Role for Pain Catastrophizing

Pain is an inherently negative perceptual and affective experience that acts as a warning system to protect the body from injury and illness. Pain unfolds over time and is influenced by myriad factors, making it highly dynamic. Despite this, statistical measures often treat any intraindividual variability in pain ratings as noise or error. This is consequential, especially for research on chronic pain, because pain variability is associated with greater pain severity and depression. Yet, differences in pain variability between patients with chronic pain and controls in response to acute pain has not been fully examined-and it is unknown if dispositional factors such as pain catastrophizing (negative cognitive-affective response to potential or actual pain in which attention cannot be diverted away from pain) relate to pain variability. In the current study, we recruited chronic-pain patients (N = 30) and pain-free controls (N = 22) to complete a 30-second thermal pain task where they continually rated a painful thermal stimulus. To quantify pain variability and capture potential dynamics, we used both a traditional intraindividual standard deviation (iSD) metric of variability and a novel derivatives approach. For both metrics, patients with chronic pain had higher variability in their pain ratings over time, and pain catastrophizing significantly mediated this relationship. This suggests patients with chronic pain experience pain stimuli differently over time, and pain catastrophizing may account for this differential experience. Perspective: The present study demonstrates (using multiple variability metrics) that chronic pain patients show more variability when rating experimental pain stimuli, and that pain catastrophizing helps explain this differential experience. These results provide preliminary evidence that short-term pain variability could have utility as a clinical marker in pain assessment and treatment. (c) 2024 (c) Published by Elsevier Inc. on behalf of United States Association for the Study of Pain, Inc All rights are reserved, including those for text and data mining, AI training, and similar technologies.